Assessing the Effectiveness of Multi-Management Practices on Water Quality

Funded by the Iowa Nutrient Reduction Center this project aims to: 1) document prediction accuracy of APSIM in terms of N leaching and crop yield across eight fields that represent different regions in Iowa, and 2) quantify via modeling the effectiveness of individual vs stacked practices on water quality and profitability and rank them from high to low effectiveness per region.   

Engineering Crops for Genetic Adaptation to Changing Environments

Funded by NSF (LEAP-HI/GOALI) this project aims to: 1) accurately predict plant phenotypes based on genetic, agronomic management and environmental data; 2) design cultivars with superior phenotypes; 3) design crop management strategies to assure that crops achieve superior phenotypes under changing environments. The team will use crop modeling, machine learning and optimization tools to analyze public and industry datasets towards designing new crop varieties for genetic adaptation to changing environments. Project PI: Lizhi Wang, co-PIs: Guiping Hu, Bill Beavis, and Sotirios Archontoulis

Evaluating the relative influence of maize breeding, field management, and environmental setting on crop production and sustainability targets

Funded by FFAR and Bayer Crop Science this project will 1) determine changes in plant traits over 40 years of plant breeding and genetic gain in Bayer’s legacy hybrids; 2) determine the impact of plant breeding on sustainability and rank the importance of different plant traits with respect to production and sustainability; and 3) predict future yield trends and environmental outcomes under a range of scenarios in the US Corn Belt. More info here.

This project developed to forecast and benchmark in soil water and nitrogen dynamics across scales and disseminate information in real-time to support decision making. Currently the FACTS website host the following tools: 1) Field scale soil water, nitrogen, phenology and crop yield forecast, 2) Regional scale benchmarking of soil water, temperature and soil N mineralization, 3) Corn dry down calculator, 4) Weather assessment tool at crop reporting district level 

Improving Simulation of Soil Water Dynamics and Crop Yields in the US Corn Belt

Funded by FFAR, New Innovator Award, this project aims to 1) develop protocols to enable simulation of water table dynamics in crop models across the landscape; 2) develop new mechanisms to relate excessive moisture impacts on soil-plant-atmosphere processes; and 3) improve and expand the coverage of the FACTS forecasting and assessment cropping systems web-tool that provides real-time predictions. 

Maize Leaf Appearance Rate

Funded by NSF EAGER this project aims to 1) extract leaf appearance rates from images and train a genomic prediction algorithm to predict leaf appearance rate from genotypic data and 2) combine the genomic prediction algorithm with the APSIM model to perform what-if scenario analysis at scale. Project PI: Lizhi Wang, Co-PIs: Sotirios Archontoulis, Guiping Hu, Patrick Schnable, Baskar Ganapathysubramanian

Managing Crop Residues To Maximize Grain Production And Environmental Performance

Funded by USDA-NIFA this project aims to: 1) conduct field and lab experiments to develop new fundamental knowledge on corn and soybean residue decomposition dynamics at the organ level; 2) incorporate experimental results into a dynamic simulation model (APSIM) to improve prediction and explanatory power; and 3) deploy the improved model to extrapolate knowledge across environments, identifying residue management strategies that are profitable in the near-term and sustainable in the long-term. Project PIs: Sotirios Archontoulis and Mike Castellano

Modeling Maize Yield Response to Nitrogen and Crop Rotation

This project aims to 1) test APSIM's ability in simulating long term corn yield response to nitrogen and crop rotation using data from 14 locations (each with 10 to 20 years of data) in Iowa and Illinois, 2) use the model to better understand the yield response to N relationship, and 3) explore predictability of economic optimum N rate. This is a collaborative project with John Sawyer and Emerson Nafziger.  

Modeling roots and root to shoot ratio in APSIM Next Gen

This project aims to test and potentially improve the simulation of root attributes in APSIM Next Gen models: maximum depth, root front velocity, root mass production and decomposition, root length, root N and C concentration per soil layer and total. Project team: Sotirios Archontoulis, Elvis Elli, Neil Huth and Dean Holzworth. The project is partially funded by the APSIM Initiative.   

Plant Arrangements Effects on Maize Morphology, Physiology, and Yields

Funded by Stine Seeds this project aims to 1) determine yield differences among six production systems having different plant arrangements and genotypes at scale, 2) perform a systems analysis to quantify how different plant arrangements and genotypes affect phenological, morphological, biomass production, biomass partitioning (including roots), and N-related traits.

Soybean Protein

Funded by USB this project aims to investigate how management practices, environmental factors, and cultivars influence soybean seed protein. This is an multi-state collaborative research effort lead by Ignacio Ciampitti at Kansas State University